Co-Founder and Chief Data Scientist at TrustInsights.ai
Christopher S. Penn is an authority on analytics, digital marketing, marketing technology, data science, and machine learning. A recognized thought leader, best-selling author, and keynote speaker, he has shaped five key fields in the marketing industry: Google Analytics adoption, data-driven marketing, and PR, modern email marketing, marketing data science, and artificial intelligence/machine learning in marketing. As co-founder and Chief Data Scientist of Trust Insights, he is responsible for the creation of products and services, creation and maintenance of all code and intellectual property, technology and marketing strategy, brand awareness, and research & development.
How did you get to become an expert in your key topics?
How did I become an expert in my key topics, a lot of what I do. Didn’t really exist when I was in school and things and even in my first jobs. And so. A lot of this it’s trial and error, a lot of it’s learning as you go researching, having interesting problems to solve, I think that’s probably the most useful and the most practical way to learn a lot about marketing data science because as a field didn’t really exist until a few years ago, data science itself is a relatively new phenomenon. We’ve had obviously statistics and probability for, you know, centuries, if not millennia.
But data science as a profession is relatively new. It’s, you know, 20 some odd years old at most, and marketing data science is almost totally brand new because of marketing. Marketing really hasn’t been analytically focused until the internet era.
And even then, major things like, you know, multi-touch attribution are less than a decade old in a lot of cases, actually a little bit more than a decade old. But for the most part, all of these things are new.
And that in turn, means that we have not learned a lot of this stuff yet. We have not processed a lot of it. And so it’s brand new to everybody. And as we just got to learn it as you go. See this in the video.
What sub-topics are you most passionate about?
Within marketing, data science, and artificial intelligence, there are so many interesting sub-topics within analytics. There are so many interesting sub-topics. one of my favorites is natural language processing. I’m fascinated by the way that machines perceive language and writing in a way that is different than we as humans perceive it. But yet the machines are rapidly catching up in terms of their ability to process language and turn it into something useful. Right? That’s really the key is what we produce has to be useful. If it’s just piles and numbers and things, that doesn’t help us.
But being able to transcribe text accurately translate it from one language to another, generate net new text based on existing text. That’s where the value of all this artificial intelligence really comes out and where these advanced techniques can help us lend insights and create insights that we can take action on. Simple example In most organizations, there is a customer service inbox, right? There’s a place where people go to email the company, and all that text is free form, and it’s usually very, very messy. What if you could unlock that data that is in those places because it’s free from what if you could extract out insights about how many people are complaining about this or how many people like that, or how many people had a good experience with service or bad experience with service in ways that might not necessarily be obvious? And then calibrate that against things like star ratings, one-star, two-stars, three-star, five stars in reviews, and that sort of natural language processing, really. Brings to life customer feedback in a way that we can take action on. We can do something with it. If you run a natural language processing analysis and you find that all your one-star reviews are about, make something up a donut topping that nobody liked.
Knowing that being able to get the insight faster and spend less money on market research and have it be accurate is super valuable. And so in that sub-topic, that’s where you’re going to see a lot of interesting growth. I think a lot of practical tools and practical applications, that’s the part that I personally know a lot of focused on. A lot is the practical application of the stuff. How do we turn this into something that you can make a decision with because analytics is great? But if you don’t take action, it doesn’t mean anything. See this in the video.
Who influences you within these topics?
Who is influential in these topics? There are so many. So many different companies and software platforms and stuff, but I tend to pay the most attention to academic or near academic sources.
Websites like Katie Nuggets, for example, and toward data science and the toward data science, discord and lot the software packages. So I write most of my software in the R programing language and am able to pick out new packages, new libraries as they come out, test them out, see how they work, and run them on known existing data sources. That’s where I get the newest ideas and certainly from a lot of events and seeing what’s being presented at events like new trips, for example, the new trips conference, as well as companies that are bringing this stuff into production companies like IBM, for example, I’m an IBM champion company doing a lot in natural language processing like market news and lately, and a bunch of others in the space. Those would be influences in terms of where I go to learn stuff.
I don’t really buy much in the space because. Most of the time, I’m trying to learn new tools, new techniques, new tactics for applying this technology, and so I follow and pay attention to people in publications that offer. New points of view as opposed to new products or services. See this in the video.
What do you think the future holds in this space?
What does the future look like for marketing, data science, and marketing? It’s going to be interesting.
I think that there are certainly a lot of technology vendors in the space that are trying to sell point solutions for every little task. Some will succeed with some or not. I think at a certain point they will be they’ll have to be some level of consolidation, some level of acquisitions and mergers and things to bring a lot of these individual point solutions to an umbrella. And that’s how you get things like Adobe Marketing Cloud and Salesforce Marketing Cloud and so on and so forth to get all of these clouds.
But the future, in general, will be constrained by those same limitations of people and processes if your company has the wrong people with the wrong strategy in place and your internal processes for handling data are broken. No amount of artificial intelligence and machine learning is going to be out to fix that and make it usable in the same way that no matter how good a chef you are, you cannot bake a cake with sand. You just can’t do it. The ingredients are wrong. And so. I think the companies that will succeed in applying A.I. and machine learning and data science to their marketing challenges are those companies that already have centers of excellence in just data management in general. They know where their data is, they know how it’s stored. They have good processes in place to manage it. They have the right people to at least keep the trains on the rails when it comes to marketing data. And those companies will succeed. And then there’ll be a whole bunch of companies that will be scrambling to keep up. one of the challenges that companies will have in marketing data is that, as with all things in machine learning and A.I., the more data you have, the better your algorithms tend to do.
All other things being equal, the more good data you have, the better and easier it is to build credible working models. The sooner you start collecting good data, the easier it is for you to build those models. And that gives you a head start that gives you an advantage. And so a competitor who is behind right, who is not keeping up, who is not collecting that data. They keep falling further and further behind every single day that a good competitor is collecting data and using it to build models. And catching up in that space is very difficult. It’s one of the reasons why you don’t see really strong competition against the big tech companies, right? It’s not because those big tech companies are better companies per se, but because so much of what they do is powered by artificial intelligence and they have a decade or two decades of data as their backbone. So it’s very hard for a new entrant to enter the space. See this in the video.
What brands are leading the way in this space?
What brands are leading the way in AI and machine learning, and gosh, there are so many, but it’s all the big technology companies that you know of Amazon, Google, Facebook, IBM, Apple, right?
it’s a big technology company, you know, Microsoft, all these companies and brands are leading a lot of the commercialization of this technology. And then you also have an enormous amount of open source projects and software and developers and creators that are at the very cutting edge.
These are folks that are creating models. Aren’t necessarily ready for prime time, but you can see hints of wit where the technology is going and those are things to pay attention to as well, because if it’s happening in academia now, you know, in two or three years, it’ll probably be commercial.
And if you have your ear to the ground and you are collecting information and you are, you’re doing a good job of making sure your data is clean when those models become available. As soon as they’re available, even if they’re not ready for prime time, if you have the right people in place, you can take advantage of them much sooner and jump ahead of the crowd, if you will, any time there’s a model that could provide substantial benefit to a business. And I should say that just for folks who are not in the data science and air space.
When I say the word model like a machine learning model, we’re really just talking about a piece of software. No different than Microsoft Word, right? It’s just that instead of humans writing it, which humans wrote in Microsoft Word, these A.I. models are software written by machines for machines. That’s all they are. See this in the video.
If a brand wanted to work with you, which activities would you be most interested in collaborating on?
If a brand wanted to work with me in terms of activities, gosh, I mean, pretty much happy to try out anything podcasts, webinars, conferences, speaking on stage beta testing. I do a lot of work with IBM, for example, as a sponsor user, so I talk to their development teams and give early product feedback and stuff.
It really is depending on what the brand has gotten, whether it is real and credible and good and useful, and it doesn’t have to be, you know, some massive new product relaunch. It can be to say, Hey, we’ve got this cool little new widget. What do you think about it taking a look at it? I will take a look at anything that’s credible and that is aligned with my personal and professional interests. See this in the video.
What are your passions outside of work?
Passions outside of work. This there’s so many do to tackle this, things like the martial arts, there’s writing fiction, there’s writing nonfiction, there is music and stuff.
I am a camera and microphone and keyboard addict. I am constantly trying out new gadgets all the time just to see if they are worth it, they live up to the hype that the brands put out, and sometimes they are. Sometimes they’re not. I’m actually trying out a new microphone to see under my collar here and seeing these new wireless microphones do what they say they’re supposed to do. So lots and lots of fun things that eventually in some way find their way back to commercial and world usage. I was helping moderate a fan fiction writing contest at one point, and I wrote some code for analyzing the linguistic characteristics, natural language processing around the contest entries.
What would be the best way for a brand to contact you?
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